Temporal disambiguation of relative temporal expressions in clinical texts using temporally fine-tuned contextual word embeddings.

Temporal reasoning is the ability to extract and assimilate temporal information to reconstruct a series of events such that they can be reasoned over to answer questions involving time. Temporal reasoning in the clinical domain is challenging due to specialized medical terms and nomenclature, shorthand notation, fragmented text, a variety of writing styles used by different medical units, redundancy of information that has to be reconciled, and an increased number of temporal references as compared to general domain texts. Work in the area of clinical temporal reasoning has progressed, but the current state-of-the-art still has a ways to go before practical application in the clinical setting will be possible. Much of the current work in this field is focused on direct and explicit temporal expressions and identifying temporal relations. However, there is little work focused on relative temporal expressions, which can be difficult to normalize, but are vital to ordering events on a timeline. This work introduces a new temporal expression recognition and normalization tool, Chrono, that normalizes temporal expressions into both SCATE and TimeML schemes. Chrono advances clinical timeline extraction as it is capable of identifying more vague and relative temporal expressions than the current state-of-the-art and utilizes contextualized word embeddings from fine-tuned BERT models to disambiguate temporal types, which achieves state-of-the-art performance on relative temporal expressions. In addition, this work shows that fine-tuning BERT models on temporal tasks modifies the contextualized embeddings so that they achieve improved performance in classical SVM and CNN classifiers. Finally, this works provides a new tool for linking temporal expressions to events or other entities by introducing a novel method to identify which tokens an entire temporal expression is paying the most attention to by summarizing the attention weight matrices output by BERT models

Semantics and statistics for automated image annotation

Automated image annotation consists of a number of techniques that aim to find the correlation between words and image features such as colour, shape, and texture to provide correct annotation words to images. In particular, approaches based on Bayesian theory use machine-learning techniques to learn statistical models from a training set of pre-annotated images and apply them to generate annotations for unseen images. The focus of this thesis lies in demonstrating that an approach, which goes beyond learning the statistical correlation between words and visual features and also exploits information about the actual semantics of the words used in the annotation process, is able to improve the performance of probabilistic annotation systems. Specifically, I present three experiments. Firstly, I introduce a novel approach that automatically refines the annotation words generated by a non-parametric density estimation model using semantic relatedness measures. Initially, I consider semantic measures based on co-occurrence of words in the training set. However, this approach can exhibit limitations, as its performance depends on the quality and coverage provided by the training data. For this reason, I devise an alternative solution that combines semantic measures based on knowledge sources, such as WordNet and Wikipedia, with word co-occurrence in the training set and on the web, to achieve statistically significant results over the baseline. Secondly, I investigate the effect of using semantic measures inside an evaluation measure that computes the performance of an automated image annotation system, whose annotation words adopt the hierarchical structure of an ontology. This is the case of the ImageCLEF2009 collection. Finally, I propose a Markov Random Field that exploits the semantic context dependencies of the image. The best result obtains a mean average precision of 0.32, which is consistent with the state-of-the-art in automated image annotation for the Corel 5k dataset. </br

Functional MRI investigations of overlapping spatial memories and flexible decision-making in humans

Thesis (Ph.D.)--Boston UniversityResearch in rodents and computational modeling work suggest a critical role for the hippocampus in representing overlapping memories. This thesis tested predictions that the hippocampus is important in humans for remembering overlapping spatial events, and that flexible navigation of spatial routes is supported by key prefrontal and striatal structures operating in conjunction with the hippocampus. The three experiments described in this dissertation used functional magnetic resonance imaging (fMRI) in healthy young people to examine brain activity during context-dependent navigation of virtual maze environments. Experiment 1 tested whether humans recruit the hippocampus and orbitofrontal cortex to successfully retrieve well-learned overlapping spatial routes. Participants navigated familiar virtual maze environments during fMRI scanning. Brain activity for flexible retrieval of overlapping spatial memories was contrasted with activity for retrieval of distinct non-overlapping memories. Results demonstrate the hippocampus is more strongly recruited for planning and retrieval of overlapping routes than non-overlapping routes, and the orbitofrontal cortex is recruited specifically for context-dependent navigational decisions. Experiment 2 examined whether the hippocampus, orbitofrontal cortex, and striatum interact cooperatively to support flexible navigation of overlapping routes. Using a functional connectivity analysis of fMRI data, we compared interactions between these structures during virtual navigation of overlapping and non-overlapping mazes. Results demonstrate the hippocampus interacts with the caudate more strongly for navigating overlapping than non-overlapping routes. Both structures cooperate with the orbitofrontal cortex specifically during context-dependent decision points, suggesting the orbitofrontal cortex mediates translation of contextual information into the flexible selection of behavior. Experiment 3 examined whether the hippocampus and caudate contribute to forming context-dependent memories. fMRI activity for learning new virtual mazes which overlap with familiar routes was compared with activity for learning completely distinct routes. Results demonstrate both the hippocampus and caudate are preferentially recruited for learning mazes which overlap with existing route memories. Furthermore, both areas update their responses to familiar route memories which become context-dependent, suggesting complementary roles in both learning and updating overlapping representations. Together, these studies demonstrate that navigational decisions based on overlapping representations rely on a network incorporating hippocampal function with the evaluation and selection of behavior in the prefrontal cortex and striatum

Compiler-managed memory system for software-exposed architectures

Thesis (Ph.D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2000.Includes bibliographical references (p. 155-161).Microprocessors must exploit both instruction-level parallelism (ILP) and memory parallelism for high performance. Sophisticated techniques for ILP have boosted the ability of modern-day microprocessors to exploit ILP when available. Unfortunately, improvements in memory parallelism in microprocessors have lagged behind. This thesis explains why memory parallelism is hard to exploit in microprocessors and advocate bank-exposed architectures as an effective way to exploit more memory parallelism. Bank exposed architectures are a kind of software-exposed architecture: one in which the low level details of the hardware are visible to the software. In a bank-exposed architecture, the memory banks are visible to the software, enabling the compiler to exploit a high degree of memory parallelism in addition to ILP. Bank-exposed architectures can be employed by general-purpose processors, and by embedded chips, such as those used for digital-signal processing. This thesis presents Maps, an enabling compiler technology for bank-exposed architectures. Maps solves the problem of bank-disambiguation, i.e., how to distribute data in sequential programs among several banks to best exploit memory parallelism, while retaining the ability to disambiguate each data reference to a particular bank. Two methods for bank disambiguation are presented: equivalence-class unification and modulo unrolling. Taking a sequential program as input, a bank-disambiguation method produces two outputs: first, a distribution of each program object among the memory banks; and second, a bank number for every reference that can be proven to access a single, known bank for that data distribution. Finally, the thesis shows why non-disambiguated accesses are sometimes desirable. Dependences between disambiguated and non-disambiguated accesses are enforced through explicit synchronization and software serial ordering. The MIT Raw machine is an example of a software-exposed architecture. Raw exposes its ILP, memory and communication mechanisms. The Maps system has been implemented in the Raw compiler. Results on Raw using sequential codes demonstrate that using bank disambiguation in addition to ILP improves performance by a factor of 3 to 5 over using ILP alone.by Rajeev Barua.Ph.D

Posture Estimation for Improved Photogrammetric Localization of Pedestrians in Monocular Infrared Imagery

Target tracking within conventional video imagery poses a significant challenge that is increasingly being addressed via complex algorithmic solutions. The complexity of this problem can be fundamentally attributed to the ambiguity associated with actual 3D scene position of a given tracked object in relation to its observed position in 2D image space. Recent work has tackled this challenge head on by returning to classical photogrammetry, within the context of current target detection and classification techniques, as a means of recovering the true 3D position of pedestrian targets within the bounds of current accuracy norms. A key limitation in such approaches is the assumption of posture – that the observed pedestrian is at full height stance within the scene. Whilst prior work has shown the effects of statistical height variation to be negligible, variations in the posture of the target may still pose a significant source of potential error. Here we present a method that addresses this issue via the use of regression based pedestrian posture estimation. This is demonstrated for variations in pedestrian target height ranging from 0.4-2m over a distance to target range of 7-30m